Risk-Calibrated World-Model Gates

Integrate this at the acceptance stage of a learned simulator or Code World Model, immediately after training and before allowing a planner to use the model. First define an event detector E(s,a,s') for mode-relevant behavior: contact with a boundary, abrupt velocity reset, termination, saturation, collision, or a large disagreement between independently trained predictors. Estimate r under the current gate distribution by counting E, but do not trust the estimate when zero events are observed. Use a conservative lower confidence estimate r_low from a Beta(1,1) prior or a stress-test distribution. If the target miss probability is delta, set N=ceil(log(delta)/log(1-r_low)). Run N ordinary rollouts and K planner-directed rollouts initialized near the highest predicted value-gradient, uncertainty, or boundary score. Reject the model if any transition violates the reference environment or if the estimated high-cost risk C_hat*(1-r_low)^N exceeds a deployment threshold. Pseudocode: sample or generate states z_i; execute reference and learned transitions; compute E_i and costs C_i; update r_low; compute N_required; accept only when N>=N_required and C_hat*(1-r_low)^N<=R_max. The exact mathematics supplies the miss probability and required sample count; r_low, C_hat, and the stress distribution are empirical. First test on a cart-with-wall or small MuJoCo contact task using an MLP simulator and random-gate baseline. Measure accepted blind models, contact detection rate, false rejection rate, and planner regret. Success is fewer catastrophic accepted models at the same verification budget, or a large regret reduction for a modest rollout increase.